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Last updated on 2022-08-07.

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Tural Sadigov

Statistician, Mathematician and aspiring Data Scientist

Professional Summary

Bio

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  • Visiting Assistant Professor of Mathematics and Statistics with Ph.D. in Applied Mathematics and 5+ years experience in teaching data-related courses such as various levels of Statistics, Machine Learning, Probability and Time Series Analysis, and mentoring undergraduate Machine Learning projects.

Current positions

Visiting Assistant Professor of Mathematics and Statistics

Hamilton College

Clinton, NY

present - 2020

  • Teaching data science courses including Statistical Modeling and Its Applications and Statistical Methods in Machine Learning. Also supervising students’ machine learning projects.
  • Also teaching other mathematics courses such as all levels of calculus

Coursera Instructor

Coursera

Online

present - 2017

  • Co-teaching Practical Time Series Analysis with William Thistleton

Previous positions

Lecturer of Statistics

Hamilton College

Clinton, NY

2020 - 2019

  • Teaching Statistical Analysis of Data

Lecture of Applied Mathematics

SUNY Polytechnic Institue

Utica, NY

2020 - 2015

  • Teaching data science, statistics and applied mathematics courses
  • Applied Probability, Regression, Time Series Analysis, Linear Algebra, Calculus, Differential Equations
  • Both undergraduate and graduate level courses

Mathematics Service Coordinator

SUNY Polytechnic Institue

Utica, NY

2020 - 2019

  • Organizing 100 and 200 level mathematcs courses and supervising instructors teaching them

Associate Instructor

Indiana University

Bloomington, IN

2015 - 2008

  • Teaching Assistant
  • 100 and 200 level mathematics courses

Education

PhD., Applied Mathematics & M.A., Mathematics

Indiana University

Bloomington, IN

2015 - 2008

  • Focused on data assimilation and determining forms for semidissipative dispersive systems
  • Associate Instructor

B.S., Mathematics

Boğaziçi University

Istanbul, Turkey

2008 - 2003

  • Mathematics

Data Science Skills

Supervised Learning

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  • Supervised Learning: Multiple Linear Regression, Multiple Logistic Regression, Polynomial Regression, Regularized models, KNN regression and classification, Tree Based Methods (Decision Trees, Random Forest, Boosted Trees), Support Vector Machines, Naive Bayes Classifier;

Unsupervised Learning

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  • k-means clustering, Hierarchical clustering , PCA

Data cleaning & Feature Engineering

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  • Numerical and categorical variable imputation, variable transformation, filtering

Time Series Analysis

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  • ARIMA, SARIMA

Research Experience

Summer Research Associate

Air Force Research Lab Griffis Institute

Rome, NY

2021

  • Worked on theoretical neural network solutions of dispersive equations
  • Supervised a student who worked with me

Summer Research Associate

Air Force Research Lab Griffis Institute

Rome, NY

2020

  • Worked on numerical algorithms to solve partial differential equations

Awards/Grants

Hamilton College

Dean’s Pedagogical Development Award (twice)

Clinton, NY

2022 - 2021

Air Force Reserach Lab (AFRL/RI)

Summer research grants (twice)

Rome, NY

2021 - 2020

SUNY Polytechnic Institue

SGU Award for Excellence in Teaching

Utica, NY

2019 - 2018

Coursera

Teaching Grant (Practical Time Series Analysis)

Online

2017

IMO 2003

Bronze medal

Tokyo, Japan

2003

Ministry of Science and Education Republic of Azerbaijan

Four Gold medals

Baku, Azerbaijan

2003 - 2000

IMO 2002

Participation

Glasgow, Scotland

2002



Mentored Machine Learning Projects

Available at request.

Publications, Invited Talks, References

Available at request.